The problem addressed by image stabilization dates from the beginning of photography, and it is basically caused by the fact that any known image sensor needs to have the image projected on it during a period of time called an integration (exposure) time. Any motion of the camera and/or of the object during this time can cause a shift of the image projected on the sensor, resulting in a degradation of the final image called a motion blur.
The ongoing development and miniaturization of consumer devices that have image acquisition capabilities increases the need for robust and efficient image stabilization solutions. The requirements may include:                Longer integration times in order to cope with smaller pixel areas that result from sensor miniaturization and resolution increase requirements;        Longer integration times in order to acquire better pictures in low light conditions;        Avoiding unwanted motion during the integration time when using high zoom, and/or small hand-held devices, etc.        
A simple way to prevent the motion blur is to set a short exposure time for the image. However, in the absence of good illumination, such short-exposed picture will be very noisy due to various noise factors (e.g. sensor noise, photon-shot noise, quantization noise, etc.).
In order to cope with the noise one can adopt a so called “multi-frame image stabilization” solution in which multiple short exposed images are aligned and combined together in order to synthesize a single long exposed image.
Alternatively, if the exposure time of the camera is set longer, then the acquired image will be less affected by noise but it could be degraded by the motion blur due to arbitrary camera motion during the exposure time. In order to restore such an image it is necessary to have accurate knowledge about the motion that took place during the exposure time. A special case of such “single-frame image stabilization” solutions are known under the generic name “opto-mechanical image stabilizers”. These stabilizers are implemented by several vendors (e.g., CANON, PANASONIC, KONIKA-MINOLTA, etc.), and they are based on moving either the optics or the image sensor in the opposite direction of the camera motion in order to keep the image projected on the sensor in the same position during the exposure time. The method copes only with camera motion being unable to correct the blur caused by moving objects in the scene. In addition, the method has also other disadvantages like: difficulty to maintain stability during longer exposure times, and inability to cope with other motion models (e.g., rotations) than translational motion. On top of these there are also size, and cost issues related with optical stabilizers for mobile devices.
Most image sensors are using the same exposure time for all pixels. However, not all image pixels are affected by motion blur at the same extent. For instance, pixels that record smooth image areas are much less affected by motion blur than the pixels localized in the neighborhood of moving object boundaries (i.e. moving edges). Also, if the camera is fixed, the motion blur can be created only by fast moving objects passing in front of the camera, and not by the static background. In such a case only the pixels that represent the image of the moving object(s) are affected by motion blur.
One approach for stabilization would be to set the exposure time of different pixels, dynamically, during image capturing, based on the actual scene content and dynamics. This approach is indeed in accordance to the fact that some image pixels are less affected by motion blur than others. However, such an approach turns out to be quite inefficient since it requires monitoring the charge of all image pixels during exposure in order to decide whether or not some motion occurs on any of them in order to stop its exposure.